Biblio

The expression of cyber-attacks on communication links in smart grids has emerged recently. In microgrids, cooperation between agents through communication links is required, thus, microgrids can be considered as cyber-physical-systems and they are vulnerable to cyber-attack threats. Cyber-attacks can cause damages in control systems, therefore, the resilient control methods are necessary. In this paper, a resilient control approach against false data injection attack is proposed for secondary control of DC microgrids. In the proposed framework, a PI controller with an adjustable gain is utilized to eliminate the injected false data. The proposed control method is employed for both sensor and link attacks. Convergence analysis of the measurement sensors and the secondary control objectives under the studied control method is performed. Finally, a DC microgrid with four units is built in Matlab/Simulink environment to verify the proposed approach.

The prevalent use of mobile applications using location information to improve the quality of their service has arisen privacy issues, particularly regarding the extraction of user's points on interest. Many studies in the literature focus on presenting algorithms that allow to protect the user of such applications. However, these solutions often require a high level of expertise to be understood and tuned properly. In this paper, the first control-based approach of this problem is presented. The protection algorithm is considered as the ``physical'' plant and its parameters as control signals that enable to guarantee privacy despite user's mobility pattern. The following of the paper presents the first control formulation of POI-related privacy measure, as well as dynamic modeling and a simple yet efficient PI control strategy. The evaluation using simulated mobility records shows the relevance and efficiency of the presented approach.

The concept of Virtualized Network Functions (VNFs) aims to move Network Functions (NFs) out of dedicated hardware devices into software that runs on commodity hardware. A single NF consists of multiple VNF instances, usually running on virtual machines in a cloud infrastructure. The elastic management of an NF refers to load management across the VNF instances and the autonomic scaling of the number of VNF instances as the load on the NF changes. In this paper, we present EL-SEC, an autonomic framework to elastically manage security NFs on a virtualized infrastructure. As a use case, we deploy the Snort Intrusion Detection System as the NF on the GENI testbed. Concepts from control theory are used to create an Elastic Manager, which implements various controllers - in this paper, Proportional Integral (PI) and Proportional Integral Derivative (PID) - to direct traffic across the VNF Snort instances by monitoring the current load. RINA (a clean-slate Recursive InterNetwork Architecture) is used to build a distributed application that monitors load and collects Snort alerts, which are processed by the Elastic Manager and an Attack Analyzer, respectively. Software Defined Networking (SDN) is used to steer traffic through the VNF instances, and to block attack traffic. Our results show that virtualized security NFs can be easily deployed using our EL-SEC framework. With the help of real-time graphs, we show that PI and PID controllers can be used to easily scale the system, which leads to quicker detection of attacks.

This paper presents the results of research and simulation of feature automated control of a hysteretic object and the difference between automated control and automatic control. The main feature of automatic control is in the fact that the control loop contains human being as a regulator with its limited response speed. The human reaction can be described as integrating link. The hysteretic object characteristic is switching from one state to another. This is followed by a transient process from one to another characteristic. For this reason, it is very difficult to keep the object in a desired state. Automatic operation ensures fast switching of the feedback signal that produces such a mode, which in many ways is similar to the sliding mode. In the sliding mode control signal abruptly switches from maximum to minimum and vice versa. The average value provides the necessary action to the object. Theoretical analysis and simulation show that the use of the maximum value of the control signal is not required. It is sufficient that the switching oscillation amplitude is such that the output signal varies with the movement of the object along both branches with hysteretic characteristics in the fastest cycle. The average output value in this case corresponds to the prescribed value of the control task. With automated control, the human response can be approximately modeled by integrating regulator. In this case the amplitude fluctuation could be excessively high and the frequency could be excessively low. The simulation showed that creating an artificial additional fluctuation in the control signal makes possible to provide a reduction in the amplitude and the resulting increase in the frequency of oscillation near to the prescribed value. This should be evaluated as a way to improve the quality of automated control with the helps of human being. The paper presents some practical examples of the examined method.

This paper develops a model for Wells turbine using Xilinx system generator (XSG)toolbox of Matlab. The Wells turbine is very popular in oscillating water column (OWC) wave energy converters. Mostly, the turbine behavior is emulated in a controlled DC or AC motor coupled with a generator. Therefore, it is required to model the OWC and Wells turbine in real time software like XSG. It generates the OWC turbine behavior in real time. Next, a PI control scheme is suggested for controlling the DC motor so as to emulate the Wells turbine efficiently. The overall performance of the system is tested with asquirrel cage induction generator (SCIG). The Pierson-Moskowitz and JONSWAP irregular wave models have been applied to validate the OWC model. Finally, the simulation results for Wells turbine and PI controller have beendiscussed.